Cardiovascular disease is one of the most common diseases in the modern world, which, if diagnosed early, can greatly reduce the damage to the patient. Diagnosis of heart disease requires great care, and in some cases, the process can be disrupted by human error. Machine learning methods, especially data mining, have gained international acceptance in almost all aspects of life, especially the prediction of heart disease. On the other hand, datasets related to heart patients have many biological features that most of these features do not have a direct impact on diagnosis. By removing redundant features from the dataset, in addition to reducing computational complexity, the accuracy of heart patients’ predictions can also be increased. This paper presents a density-based unsupervised approach to the diagnosis of abnormalities in heart patients. In this method, the basic features in the dataset are first selected based on the filter-based feature selection approach. Then, the DBSCAN clustering method with adaptive parameters has used to increase the clustering accuracy of healthy instances and to determine abnormal instances as cardiac patients. Partition clustering methods suffer from the selection of the number of clusters and the initial central points and are very sensitive to noise. The DBSCAN method solves these problems by creating density-based clusters, but the selection of the neighborhood radius threshold and the number of connected points in the neighborhood remains unresolved. In the proposed method, these two parameters are selected adaptively to achieve the highest accuracy for the diagnosis and prediction of heart patients. The results of the experiments show that the accuracy of the proposed method for predicting heart patients is approximately 95%, which has improved in comparison with previous methods.

Online user security is strongly reliant on a number of factors. One of the most important steps in enhancing data and communication security on well-known websites and a variety of internet services is adhering to security standards and using reliable and cutting-edge technology. These standards and technologies were developed and tested recently. Our investigation at the CERTFA Lab on 50 well-known websites shows that their security is comparable to that of the rest of the world, and very few websites are completely utilizing new security standards and technology. In fact, Russia has strong and comprehensive cyber capabilities. Websites run by the Russian government are subject to unprecedented cyberattacks, and technical measures have been taken to block international online traffic. Although domestic websites have operated successfully within the nation, some security measures need to be taken more seriously. According to our investigation, 47 of the websites we looked into have been configured with CSP2, with Yandex.ru, Ozon.ru, and wileberries.ru's implementations having more specifics and being more secure than those of the other websites, which used the upgrade-insecure-requests option as the default setting for CSP. Additionally, the results of the analysis of contemporary standards used in this study (DNSSEC, CAA, DMARC, SPF, and Expect-CT), which are mandated for the majority of Internet businesses, show that well-known Russian websites have correctly implemented these standard configurations.

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